{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cec2017.functions import *\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "from ccde.algorithms import CCDE, PCCDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cec2017.functions import *\n",
    "from os.path import exists\n",
    "\n",
    "GET_FUNCTION = {\"1\": f1, \"2\": f2, \"3\": f3, \"4\": f4, \"5\": f5, \"6\": f6, \"7\": f7, \"8\": f8, \"9\": f9, \"10\": f10,\n",
    "                \"11\": f11, \"12\": f12, \"13\": f13, \"14\": f14, \"15\": f15, \"16\": f16, \"17\": f17, \"18\": f18, \"19\": f19, \"20\": f20,\n",
    "                \"21\": f21, \"22\": f22, \"23\": f23, \"24\": f24, \"25\": f25, \"26\": f26, \"27\": f27, \"28\": f28, \"29\": f29, \"30\": f30}\n",
    "\n",
    "class CCDE_cec2017:\n",
    "    def __init__(self, fnum=1, dimensions=100, default_bounds=(-100, 100), max_FES=3e+05, num_runs=31,\n",
    "                    popsize=100, mutation_rate=0.8, crossover_rate=0.9, NC=None, strategy='rand1bin',\n",
    "                    seed=None, save_file_name=None):\n",
    "        problem = GET_FUNCTION.get(str(fnum))\n",
    "        bounds = [default_bounds] * dimensions\n",
    "        fstar = fnum * 100\n",
    "        self.num_runs = num_runs\n",
    "        self.algo_name = \"CCDE\"\n",
    "        self.NC = NC\n",
    "        self.NC_text = f\"_NC{NC}\"\n",
    "        if NC == None or NC == 0:\n",
    "            self.algo_name = \"DE\"\n",
    "            self.NC_text = \"\"\n",
    "        self.model = CCDE(fobj=problem, bounds=bounds, strategy=strategy,\n",
    "             mutation=mutation_rate, crossover=crossover_rate,\n",
    "             NC=NC, maxfes=max_FES, popsize=popsize, seed=seed, fstar=fstar)\n",
    "        self.outputs = None\n",
    "        self.save_file_name = save_file_name\n",
    "        if save_file_name == None:\n",
    "            self.save_file_name = f\"results/{self.algo_name}_f{fnum}_{dimensions}D_MR{mutation_rate}_CR{crossover_rate}_{strategy}{self.NC_text}.npz\"\n",
    "        self.popsize = popsize\n",
    "        self.max_FES = max_FES\n",
    "\n",
    "    def run(self, continue_to_last_run=True, show_progressbar=True):\n",
    "        best_candidate, all_best_fitness, all_mean_fitness = [], [], []\n",
    "        last_run = 0\n",
    "        if exists(self.save_file_name) and continue_to_last_run:\n",
    "            loadnpz = np.load(self.save_file_name)\n",
    "            best_candidate, all_best_fitness, all_mean_fitness = loadnpz[\"best_candidate\"], loadnpz[\"all_best_fitness\"], loadnpz[\"all_mean_fitness\"]\n",
    "            last_run = len(best_candidate)\n",
    "            print(f\"The last saved run was {last_run} and the experiment will continue from the last...\")\n",
    "        for run in range(last_run, self.num_runs):\n",
    "            self.outputs = self.model.solve(show_progress=True)\n",
    "            \n",
    "            print(f\"Run {run+1}, Best Error: {self.outputs[1]:E}\")\n",
    "            if run != 0:\n",
    "                best_candidate = np.append(best_candidate, [self.outputs[0]], axis=0)\n",
    "                all_best_fitness = np.append(all_best_fitness, [self.outputs[2]], axis=0)\n",
    "                all_mean_fitness = np.append(all_mean_fitness, [self.outputs[3]], axis=0)\n",
    "            else:\n",
    "                best_candidate = [self.outputs[0]]\n",
    "                all_best_fitness = [self.outputs[2]]\n",
    "                all_mean_fitness = [self.outputs[3]]\n",
    "            np.savez(self.save_file_name, best_candidate=best_candidate,\n",
    "                    all_best_fitness=all_best_fitness, all_mean_fitness=all_mean_fitness)\n",
    "            \n",
    "        best_f = all_best_fitness[:, -1]\n",
    "        print(\"Best:\", \"{:e}\".format(np.amin(best_f)))\n",
    "        print(\"Median:\", \"{:e}\".format(np.median(best_f)))\n",
    "        print(\"Worst:\", \"{:e}\".format(np.amax(best_f)))\n",
    "        print(\"Mean:\", \"{:e}\".format(np.mean(best_f)))\n",
    "        print(\"Std:\", \"{:e}\".format(np.std(best_f)))\n",
    "        \n",
    "    def plot(self, figsize=(12, 5)):\n",
    "        loadnpz = np.load(self.save_file_name)\n",
    "        all_best_fitness = loadnpz[\"all_best_fitness\"]\n",
    "        \n",
    "        intervals = np.arange(self.popsize, self.max_FES+1, self.popsize)\n",
    "        fig, ax = plt.subplots(figsize=(12, 5))\n",
    "        label_text = \"\"\n",
    "        if self.NC != 0:\n",
    "            label_text = f\"({self.NC_text}-clusters)\"\n",
    "        ax.plot(intervals, np.mean(all_best_fitness, axis=0), label=f\"{self.algo_name}{label_text}\")\n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.ticklabel_format(axis='x', style='sci', scilimits=[-1,1],useMathText=True)\n",
    "        ax.set_xlabel('Function Evaluations (FES)')\n",
    "        ax.set_ylabel('f(x)-f(x*)')\n",
    "        ax.set_title('Logarithmic scale')\n",
    "        ax.grid()\n",
    "        plt.legend(loc='best')\n",
    "        plt.show()\n",
    "          \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = CCDE_cec2017(fnum=1, dimensions=100, NC=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
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     "text": [
      "Run 1, Best Error: 9.096531E+03\n"
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     "metadata": {},
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    },
    {
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     "text": [
      "Run 2, Best Error: 1.369163E+04\n"
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     "text": [
      "Run 3, Best Error: 3.906713E+03\n"
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    {
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     "text": [
      "Run 4, Best Error: 4.126603E+01\n"
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     "text": [
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     "text": [
      "Run 6, Best Error: 1.526832E+03\n"
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     "text": [
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     "text": [
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      "Run 13, Best Error: 4.964266E+00\n"
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      "Run 14, Best Error: 1.905182E+03\n"
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    {
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     "data": {
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       "model_id": "f9028537539749e1a091feb301c7b408",
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     "text": [
      "Run 25, Best Error: 4.009262E+03\n"
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    {
     "data": {
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       "model_id": "b653a084d9fc4e6d99ee497e2f606765",
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      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 28, Best Error: 1.240610E+04\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "877e9183ea214be681f47679a137a5f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 29, Best Error: 1.531933E+04\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c8243ca1f714db182a7a534a23495a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 30, Best Error: 2.276457E+00\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c56dcc4153df47d6a0b2da9f159fb2bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 31, Best Error: 1.039802E+04\n"
     ]
    }
   ],
   "source": [
    "test.run(continue_to_last_run=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = CCDE_cec2017(fnum=1, dimensions=100, NC=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The last saved run was 3 and the experiment will continue from the last...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "541b8bd090bf4dd19fca0f2a16143c64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 4, Best Error: 3.526545E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d1b3e77500d45c093aac676f72f5cf8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 5, Best Error: 2.034353E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a524af94bb141438b6411bd3b3290e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 6, Best Error: 2.784500E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a30573e93fc340f9b8def70fda81700f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 7, Best Error: 2.233611E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e722d47f83df4aadae3d46466ecf6552",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 8, Best Error: 3.229127E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8b812cd1e284a69bc9e8383ccf39750",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 9, Best Error: 3.922240E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2766bec0bdfc4dacb026516f19123641",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 10, Best Error: 2.595321E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10a14187031a415cbd1ae23dee13ce60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 11, Best Error: 3.200486E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f053512d06734221a95488e38cc3eba1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 12, Best Error: 2.887635E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0639cd0d5c744018c11ecb48ec2d26b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 13, Best Error: 2.973146E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "067c56821216479d9ba944f2e5d07ed2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 14, Best Error: 2.096951E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7e7c6e30bf2497993c27de15d70ea2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 15, Best Error: 2.335956E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9089826b7c5c479c979626a2c667c2a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 16, Best Error: 2.457406E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cefef2b00d24093942d3c68dffdec86",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 17, Best Error: 2.035789E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3ecd68bb13e47d5a654ec53991593cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 18, Best Error: 2.800199E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "800e92b0e776425f8e7a797cbc218307",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 19, Best Error: 2.607347E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6541f60b9324bdd9513177442f7ebfc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 20, Best Error: 2.527688E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c33e613bfcf48e9add8895c6ab8a9fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 21, Best Error: 3.419079E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5c77fe7d72f4cf7ab4e357bb8d6cd13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 22, Best Error: 1.773528E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3b003733e7e4054b8f780f3970ba0fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 23, Best Error: 2.649543E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a02b3390e9d4e6283d918e4fa2c0138",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 24, Best Error: 2.238601E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c2959fc323544658351714babb89a73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 25, Best Error: 2.388556E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad4ccd3168f14aeb9a2f41dc7010b857",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 26, Best Error: 2.634209E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "680d0c0c173746afba834e2bb30c2e7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 27, Best Error: 1.896984E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87057842711c4f9cb6aa4a331f38a24f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 28, Best Error: 2.437475E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f441730f35bb4351a79dd580d87e461d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 29, Best Error: 3.664983E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb5cb5a8eb5d455fba2bb887256d9dc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 30, Best Error: 2.742436E+11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c5f5fdb658a4cd0938396f352c11100",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Optimizing (CCDE):   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 31, Best Error: 1.876986E+11\n"
     ]
    }
   ],
   "source": [
    "test.run(continue_to_last_run=True, show_progressbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3d80d58c4eb30652f3d52db78929ef256a04bf7876869c9803709f526815663b"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
